In silico Bioactivity Screening and Molecular Docking of Novel Peptides from Milkfish (Chanos chanos) Muscle Proteins

Abstract

In silico approaches integrated with bioinformatics tools have created more efficient and robust methods to overcome current challenges in developing bioactive molecules discovery. Virtually extracted peptides from milkfish (Chanos chanos) are predicted to exhibit antihypertensive and antidiabetic activity from this in silico study. This study aimed to analyze the bioactivity and physiochemical properties of peptides from hydrolyzed protein sequences and to discover the potential angiotensin-converting enzyme (ACE) and Dipeptidyl peptidase-4 (DPPIV) inhibitory of novel peptides from milkfish (Chanos chanos) using machine learning based tools. Nine peptides were predicted to have ACE inhibitor activity and three to be DPPIV inhibitors. The tetra-peptides of ACE-inhibitory peptides (MVWH and PPPS) were predicted to possess a competitive mode of ACE inhibitor by directly binding to the Zn-ion tetra-coordinate. The DPPIV inhibitor peptides are predicted to inhibit the DPPIV but with no known specific mode of inhibition. PPPS peptide is the only peptide in this study that satisfied the drug-likeness analysis with no violations of the Lipinski rule of five.